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所属单位：民航学院
发表刊物：J Vib Shock
摘要：In the case of small sample size problems where only the operating data of healthy rolling bearings are available, the support vector data description (SVDD) method was applied to the rolling bearings fault detection and condition evaluation commendably by fusing multidimensional features. However, the complexity of the feature vector space distribution will directly affects the results of SVDD. Aiming at this, a novel rolling bearing fault detection method called hyper-sphere optimization support vector data description (hoSVDD) was proposed. The spatial distribution of feature vectors was improved by the hyper-sphere optimization so that the difficulty in data description was reduced. Hence, the hoSVDD is more suitable for rolling bearing fault detection. Multi-group rolling bearing tests show that: under the conditions of different speeds, different test points, and different types of rolling bearings faults, the proposed hoSVDD performs better than the traditional SVDD method. © 2019, Editorial Office of Journal of Vibration and Shock. All right reserved.
ISSN号：1000-3835
是否译文：否
发表时间：2019-01-28
合写作者：Lin, Tong,Teng, Chunyu,Wang, Yun,Ouyang, Wenli
通讯作者：陈果